{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Driven Forecasting Project Walkthrough.\n",
    "\n",
    ">>**Divyanshu Vyas | Data Science/Machine Learning for E&P**\n",
    "\n",
    "> This is a code notebook that explains how we can forecast the future values of a Time Series. \n",
    "\n",
    "A Time Series is nothing but a Sequential Dataset collected at regular time intervals. \n",
    "\n",
    "Example- Production Data with every TimeStamp is also a Time Series. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1 : Import Libraries. \n",
    "\n",
    "> Import All Libraries you need. \n",
    "1. Numpy and Pandas for Data analysis and manipulation. \n",
    "2. Statsmodels for Statistical Calculations & Evaluation of Time Series Data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RUN THIS CELL\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# Load specific forecasting tools\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf # for determining (p,q) orders\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose      # for ETS Plots\n",
    "from pmdarima import auto_arima                              # for determining ARIMA orders\n",
    "\n",
    "# Load specific evaluation tools\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import Dataset. \n",
    "\n",
    "Note that 'MS' sets the frequency of Time Series to 'Monthly Start'. Since Data is collected at Day 1 of every month.\n",
    "\n",
    "This Dataset is about Number of Employees (x1000) in a firm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>1064.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>1074.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-01</th>\n",
       "      <td>1097.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-01</th>\n",
       "      <td>1108.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Employees\n",
       "Date                 \n",
       "1990-01-01     1064.5\n",
       "1990-02-01     1074.5\n",
       "1990-03-01     1090.0\n",
       "1990-04-01     1097.4\n",
       "1990-05-01     1108.7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load datasets\n",
    "df = pd.read_csv('../Data/HospitalityEmployees.csv',index_col='Date',parse_dates=True)\n",
    "df.index.freq = 'MS'\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. A. Preliminary plotting. \n",
    "\n",
    "Trend - You can see that the Time Series is Upward Trending. I.E - The Magnitude is increasing with time. \n",
    "\n",
    "Seasonality - You can see that every year has a peak. This is called seasonality. \n",
    "\n",
    "Noise - Theres not much noise in this data. \n",
    "\n",
    "Note- Trend Seasonality and Noise are the attributes/properties of a Time Series Data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7370956d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualizing Trend and Seasonality**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Means'] = df.rolling(window=30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c739df2b20>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Employees'].plot(figsize=(12,5),legend=True)\n",
    "df['Means'].plot(figsize=(12,5),legend=True)\n",
    "ets.trend.plot(figsize=(12,5),legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot to display seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ets = seasonal_decompose(df['Employees'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c739d0bfd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ets.seasonal.plot(figsize=(16,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#You can see peaks on every year.\n",
    "#That shows an yearly seasonality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Why this is important?  - To decide a particular Time series model. eg. SARIMA where S is for Seasonal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Just an illustration of how differencing makes a Time Series Stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c73c949c40>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Employees'].plot(figsize=(12,5),label='Trending',legend=True)\n",
    "df['Employees'].diff().plot(figsize=(12,5),label='First Diff. D=1, Stationary',legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3- Now Finally Selecting a Time Series Model.\n",
    "\n",
    "Example - AR-I-MA : Here, AR means Auto Regressive. It referes to a regression fit between today's values and past values. Note that further we go in the past, these Correlations get weaker, hence we have to stop to a particular lag (time shift). This truncation point is given by the order **p**. \n",
    "\n",
    "I- Refers to How many differences we need to make to the time series to make it stationary (Horizontal Trend/No TrenD) just like a line gets horizontal by one differntiation. This order is given by **d**. \n",
    "\n",
    "MA - It is Moving Average, Whatever Relation/Forecast is not explained by AR, MA does it. Its order is given by **q**.\n",
    "\n",
    "A Library called pmdarima automatically gives us these p,d,q orders. \n",
    "\n",
    "They are important because they ultimately build the forecasting model's equation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>ARIMA</strong>, or <em>Autoregressive Integrated Moving Average</em> is actually a combination of 3 models:\n",
    "* <strong>AR(p)</strong> Autoregression - a regression model that utilizes the dependent relationship between a current observation and observations over a previous period\n",
    "* <strong>I(d)</strong> Integration - uses differencing of observations (subtracting an observation from an observation at the previous time step) in order to make the time series stationary\n",
    "* <strong>MA(q)</strong> Moving Average - a model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Math behind the models-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that an <strong>AR(1)</strong> model follows the formula\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "while an <strong>MA(1)</strong> model follows the formula\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = \\mu + \\theta_{1}\\varepsilon_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "where $c$ is a constant, $\\mu$ is the expectation of $y_{t}$ (often assumed to be zero), $\\phi_1$ (phi-sub-one) is the AR lag coefficient, $\\theta_1$ (theta-sub-one) is the MA lag coefficient, and $\\varepsilon$ (epsilon) is white noise.\n",
    "\n",
    "An <strong>ARMA(1,1)</strong> model therefore follows\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\theta_{1}\\varepsilon_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "ARMA models can be used on stationary datasets.\n",
    "\n",
    "For non-stationary datasets with a trend component, ARIMA models apply a differencing coefficient as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stp 4. Finding orders (p,d,q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                   <td>y</td>                <th>  No. Observations:  </th>    <td>348</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)x(1, 0, [1], 12)</td> <th>  Log Likelihood     </th> <td>-1098.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Sun, 20 Dec 2020</td>         <th>  AIC                </th> <td>2208.504</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>20:22:03</td>             <th>  BIC                </th> <td>2231.600</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                          <td>0</td>                <th>  HQIC               </th> <td>2217.700</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td> - 348</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.9245</td> <td>    0.045</td> <td>   20.577</td> <td> 0.000</td> <td>    0.836</td> <td>    1.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.9442</td> <td>    0.070</td> <td>  -13.437</td> <td> 0.000</td> <td>   -1.082</td> <td>   -0.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>    <td>    0.1288</td> <td>    0.060</td> <td>    2.136</td> <td> 0.033</td> <td>    0.011</td> <td>    0.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>    0.9972</td> <td>    0.001</td> <td>  706.173</td> <td> 0.000</td> <td>    0.994</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.7491</td> <td>    0.043</td> <td>  -17.319</td> <td> 0.000</td> <td>   -0.834</td> <td>   -0.664</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>   29.2963</td> <td>    1.717</td> <td>   17.061</td> <td> 0.000</td> <td>   25.931</td> <td>   32.662</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>31.49</td> <th>  Jarque-Bera (JB):  </th> <td>67.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.83</td>  <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.86</td>  <th>  Skew:              </th> <td>-0.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.40</td>  <th>  Kurtosis:          </th> <td>5.15</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                       \n",
       "============================================================================================\n",
       "Dep. Variable:                                    y   No. Observations:                  348\n",
       "Model:             SARIMAX(1, 1, 2)x(1, 0, [1], 12)   Log Likelihood               -1098.252\n",
       "Date:                              Sun, 20 Dec 2020   AIC                           2208.504\n",
       "Time:                                      20:22:03   BIC                           2231.600\n",
       "Sample:                                           0   HQIC                          2217.700\n",
       "                                              - 348                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.9245      0.045     20.577      0.000       0.836       1.013\n",
       "ma.L1         -0.9442      0.070    -13.437      0.000      -1.082      -0.806\n",
       "ma.L2          0.1288      0.060      2.136      0.033       0.011       0.247\n",
       "ar.S.L12       0.9972      0.001    706.173      0.000       0.994       1.000\n",
       "ma.S.L12      -0.7491      0.043    -17.319      0.000      -0.834      -0.664\n",
       "sigma2        29.2963      1.717     17.061      0.000      25.931      32.662\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       31.49   Jarque-Bera (JB):                67.43\n",
       "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.86   Skew:                            -0.11\n",
       "Prob(H) (two-sided):                  0.40   Kurtosis:                         5.15\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df['Employees'],seasonal=True,m=12).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hence, the suggested model is - SARIMAX(1, 1, 2)x(1, 0, [1], 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Train - Test Sequential split. \n",
    "\n",
    "> Split The data in 80-20 fraction in a sequential order, keeping the latter 20% as if it were future data (unseen). \n",
    "\n",
    "> Once we train the data on top 80%, we'll evaluate the forecasts on the latter 20% (test data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>1064.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>1074.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-01</th>\n",
       "      <td>1097.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-01</th>\n",
       "      <td>1108.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Employees\n",
       "Date                 \n",
       "1990-01-01     1064.5\n",
       "1990-02-01     1074.5\n",
       "1990-03-01     1090.0\n",
       "1990-04-01     1097.4\n",
       "1990-05-01     1108.7"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[:,:-1]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, I'm going with just 12 months as the future. so, 0-336 as train. 336-348 as test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.iloc[:336]\n",
    "test = df.iloc[336:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit a SARIMA(0,1,0)(2,0,0,12) model to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>               <td>Employees</td>            <th>  No. Observations:  </th>    <td>336</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)x(1, 0, [1], 12)</td> <th>  Log Likelihood     </th> <td>-1058.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Sun, 20 Dec 2020</td>         <th>  AIC                </th> <td>2128.116</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>20:31:05</td>             <th>  BIC                </th> <td>2151.001</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>01-01-1990</td>            <th>  HQIC               </th> <td>2137.240</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 12-01-2017</td>           <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.9241</td> <td>    0.044</td> <td>   21.209</td> <td> 0.000</td> <td>    0.839</td> <td>    1.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.9583</td> <td>    0.070</td> <td>  -13.715</td> <td> 0.000</td> <td>   -1.095</td> <td>   -0.821</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>    <td>    0.1496</td> <td>    0.060</td> <td>    2.474</td> <td> 0.013</td> <td>    0.031</td> <td>    0.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>    0.9973</td> <td>    0.001</td> <td>  726.824</td> <td> 0.000</td> <td>    0.995</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.7517</td> <td>    0.044</td> <td>  -17.154</td> <td> 0.000</td> <td>   -0.838</td> <td>   -0.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>   28.7664</td> <td>    1.684</td> <td>   17.079</td> <td> 0.000</td> <td>   25.465</td> <td>   32.068</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>29.89</td> <th>  Jarque-Bera (JB):  </th> <td>73.35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.88</td>  <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.91</td>  <th>  Skew:              </th> <td>-0.17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.63</td>  <th>  Kurtosis:          </th> <td>5.27</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                       \n",
       "============================================================================================\n",
       "Dep. Variable:                            Employees   No. Observations:                  336\n",
       "Model:             SARIMAX(1, 1, 2)x(1, 0, [1], 12)   Log Likelihood               -1058.058\n",
       "Date:                              Sun, 20 Dec 2020   AIC                           2128.116\n",
       "Time:                                      20:31:05   BIC                           2151.001\n",
       "Sample:                                  01-01-1990   HQIC                          2137.240\n",
       "                                       - 12-01-2017                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.9241      0.044     21.209      0.000       0.839       1.010\n",
       "ma.L1         -0.9583      0.070    -13.715      0.000      -1.095      -0.821\n",
       "ma.L2          0.1496      0.060      2.474      0.013       0.031       0.268\n",
       "ar.S.L12       0.9973      0.001    726.824      0.000       0.995       1.000\n",
       "ma.S.L12      -0.7517      0.044    -17.154      0.000      -0.838      -0.666\n",
       "sigma2        28.7664      1.684     17.079      0.000      25.465      32.068\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       29.89   Jarque-Bera (JB):                73.35\n",
       "Prob(Q):                              0.88   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.91   Skew:                            -0.17\n",
       "Prob(H) (two-sided):                  0.63   Kurtosis:                         5.27\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(train['Employees'],order = (1, 1, 2), seasonal_order =(1,0,1,12) )\n",
    "\n",
    "results = model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Now, our SARIMAX model of selected orders has understood the training data and has fit into it.\n",
    "\n",
    "## Step 6- Let's Make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = len(train)\n",
    "end = len(test) + len(train) - 1\n",
    "yp = results.predict(start,end,typ='levels').rename('SARIMAX-Preds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above step, the typ='levels' is provided because, the entire forecast is actually done on the differenced (And hence stationary version) of the time series. And hence, it is important to undo the differening (kind of reverse-differentiation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c73afb8070>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yMpL4+HiOHDlSNAaAlJQUdDodLi4u131Nqamp7N69m5dffpljx44RGRnJjh07sLOzY/jw4Tz99NN07NiRc+fO0bt3b6Kiopg9ezYdO3bklVdeYe3atcybN++G79W6detYtWoVERER2NvbF23z/vnnnxcF6WslJCTw/PPPExkZiZubG7169WLVqlUMGjSI7Oxs2rZty5tvvslzzz3H119/zUsvvcTrr7/O+vXr8fPzK/o6y8Pt/GtoAJ5RVXWfoihOQKSiKH8Co4FNqqq+oyjKTGAm8DyQAjygqmqCoihNgPWAX+G15gITgN1owfp+YF1pfkFCCFHhVcAgnZ6rZ0/MpaLWjqMJ6ZhUraIcGuDKE13rEB7kQVhtV+yt7+7NTgsLBW8nLQQ38y/+HFVVScvRa4E7I/dfATyPExcz2XoimZwC43XPUxTwdLT5V8X7+gDu42yLra58vp/3rGYLrXr913uw42M4sxkGfAb1Sn8nPVE6/t0Kkp6eTlpaGl26dAHgscceY9iwYUWPP/TQQwCcOHGCI0eOcN999wFgNBrx9fUlMzOT+Ph4Bg8eDICtrS0Aer2eF198kW3btmFhYUF8fDyJiYk0bdqUZ599lueff57+/fvTqVOnG8YYHBxMdHQ0U6ZMoV+/fvTqpb0bsmHDhqL7ANu3b6dFixZYWFgwc+ZMGjduzLJlyxgwYAB2dnYAbNy48br+54yMDDIzM9m2bRsrVqwAoF+/fri53TgZd+PGjTz++ONF7Snu7jdfbnLPnj107doVLy8vAEaMGMG2bdsYNGgQ1tbW9O+vrQffsmVL/vzzTwA6dOjA6NGjefDBBxkyZMhNr1+abvmvo6qqF4ALhfczFUWJQgvKA4GuhactALYCz6uquv+apx8FbBVFsQHcAWdVVXcBKIryAzAICdZCiKquAgbptJwC/om52tpx7EIGqgrWVha0CHBlcvd6tA12J6yWW7mGUUVRcHOwxs3BmkY1nYs9R1VVMvMNxbacXEjP42xqDrujU8nIM9zwXDd73fWB2/lqy8mVAH67FfgyZ2UDPV6+Wr1ePBRajNSq17Yut35+dXWLynJF4eDgAGg/z40bN2bXrl3XPZ6RkVHs8xYvXkxycjKRkZHodDoCAwPJy8ujfv36REZG8vvvv/PCCy/Qq1cvXnnlleue6+bmxsGDB1m/fj3/+9//+Pnnn/nuu+9Yt24d06dPLzrvSo91SWMGMJlM7Nq1qyhoX0tRbt66parqLc/59/kl0el0RdeytLTEYND+v//yyy+JiIhg7dq1hIaGcuDAATw8PG77Ne/WHf3roShKINACiAB8CkM3qqpeUBTFu5inDAX2q6qaryiKHxB3zWNxXK1k//t1JqBVtqlVq9adDFEIISqGmG1w6OcKE6QvZRfwT2E1end0KicSM1FVsLGyIKyWG9N61Cc82J3QANcKX9VVFAVnWx3Otjrq+ziVeF52voGLGXklBvAD59O4lF1ww/OcbK2uVrydr285uRLAnW2t7igY3BO/ljDhL/jrHdj5ydXqdd0e5fP64q64uLjg5ubG9u3b6dSpEwsXLiyqXl+rQYMGJCcns2vXLtq1a4der+fkyZM0btwYf3//opaH/Px8jEYj6enpeHt7o9Pp2LJlC2fPngW0dgl3d3ceffRRHB0dmT9/PgBOTk5kZmbi6elJSkoK1tbWDB06lDp16jB69GhUVeXQoUNFbSy3q1evXnz++efMmDED0PrJQ0ND6dy5M4sXL+all15i3bp1XL58udjnvv766wwfPvy6VpArY/238PBwpk6dSkpKCm5ubixdupQpU6bcdHxnzpwhPDyc8PBw1qxZw/nz5ytWsFYUxRH4BZimqmrGrf5BURSlMfAucOW9heKeUOyfIKqqzgPmgbal+e2OUQghzC49Hta/CMdWmTVIp2Tl80/MJSKitTB9IlH7ZWWrs6BVbXf6NfUlPNiD5gEu2FhV7CB9txxsrKjj5UgdL8cSz8nTG0nMuLHnW2tFyeP4hQySs/L5d8HM3tqyKHA/1i6QXo1rlO0Xo7OFnq9ByAParo2LhkDYY9DrDbAtvrIvzG/BggVFkxeDg4P5/vvvbzjH2tqa5cuX89RTT5Geno7BYGDatGk0btyYhQsXMnHiRF555RV0Oh3Lli1jxIgRPPDAA7Rq1YrQ0FBCQkIAOHz4MDNmzMDCwgKdTsfcuXMBmDBhAn369MHX15c5c+bw+OOPYzJp8xjefvttIiMjadGixR3/ofjpp5/y5JNP0qxZMwwGA507d+bLL7/k1Vdf5ZFHHiEsLIwuXboUWyC9//77OXDgAK1atcLa2pq+ffvy1ltvMXr0aCZNmlQ0efEKX19f3n77bbp164aqqvTt25eBAwfedHwzZszg1KlTqKpKjx49aN68+R19fXdLuZ25g4qi6IDfgPWqqn5UeOwE0LWwWu0LbFVVtUHhY/7AZuBxVVV3Fh7zBbaoqhpS+Pkjhc+feLPXbtWqlbp37967/gKFEKJcGAogYi5sfRdUI3R6Bto/pQWicpCcmV/YH51KRPQlTiVpM+/trS1pWduNtsEetA12p6mfa9WY5FeO9EYTSZn5N1S8L6bncTQhnXOXcvhgWHOGhJXQOF7qA8qDrW/B35+Bs59Wva7TrXxeu4KKioqiYcOG5h5GpfTGG29Qt25dHn74YXMPpVwV9zOjKEqkqqqtSnjKbbmdVUEU4Fsg6kqoLvQr8BjaCiCPAasLz3cF1gIvXAnVUNQukqkoSlu0VpJRwGf3MnghhKgQov+C32dAyglo0Fdbf9gtsExfMjEjTwvRMVprR3RyNgAO1pa0CnRnSJg/4cHuNPVzQWcpQfpe6Cwt8HO1w8/1xl7SnAID4xbs5ZllBykwmHi4TTm0L+ps4b7XC6vX/4GFg6Dl49Drv2BTcmuMEMV56aWXzD2EKuV2WkE6ACOBw4qiXJnu+iJaoP5ZUZSxwDngylTXyUBd4GVFUV4uPNZLVdUk4D9cXW5vHTJxUQhRmWUkwIaX4Mgv4FobHvkJGtxfJi91IT23aOm7iJhLxKRoQdrJxorWQe481CqAtsEeNK7pjJUE6XJjb23Fd6NbM3FhJDNXHKbAaGJUu8DyefGA1jBpO2x5E/7+HE5vgoGfQ/CNfbxCiPJxW60g5iStIEKICseoh4ivYOvb2v2OT0PHaaC7saJ5t+LTctl9JpWIGC1In03VtiR2trWiTZA7bYM9CA/yoFFN5+qxeUoFl28w8uTi/WyMSuSlfg0Z1ym4fAdwLgJWPwGpp6H1OOg5G2xK7i2vaqKioggJCSm/CaWiUlNVlePHj5unFUQIIcQ1YnfA2mchOQrq9YI+74L7vYeo85dyrmvtiLucC4CLnY7wIHdGtQskPMidhr4SpCsiGytL5j4axrQfD/DG2ijy9EYmd69XfgOoFQ6TdsDmN2DX/+DUnzDwfxB041rGVZGtrS2pqal4eHhIuBY3paoqqampReuClzYJ1kIIcTsyL8KGl+Hwz+BSCx5eovVT38UvcVVVOXcp57rWjvg0LUi7O1jTJtCdcR2DCA/2oIGPExYSpCsFnaUFnzwcirWVBR9sOEm+wcT0++qXX9DT2WlrXIf016rXC/prK9L0fA2sHW717ErN39+fuLg4kpOTzT0UUQnY2tri7182k40lWAshxM0YDfDPPNjyFhjzofMM6DgdrO1v+xKqqhJbuGnJleXvLmbkAeDhYE14sDsTuwQTHuRBPW9HCdKVmJWlBR8Ma461pQWfbT5NgcHEzD7l3KJQux1M2gmbXoeIL+HUBhj4BQR2KL8xlDOdTkdQUJC5hyGEBGshhCjR2b+1to+ko1CnB/R9Hzzq3NZTEzPy2BiVWFSVTsrMB7Rtt9sGuxMe7EG7YHfqeDnKW9dVjKWFwttDmmJtZcFX26LJN5h49YFG5fvf2dpe24Gw0QBt18b5fSF8EvR4pcpXr4UwJwnWQgjxb1lJ8OcrcHApuATAQ4u0t9dvIxhdTM/ji62n+fGf8xQYTfg42xSuIe1BeLA7wZ4OEqSrAQsLhdcHNsbGyoJvdsSQbzDx5qAm5f9uRO328J+dsHG2Vr0+uR4GfaEdF0KUOgnWQghxhdEAe7/VJoDpc7VNXjo9c1sVvovpeczdepql/5zHpKoMa+XP2I7B1PGSIF1dKYrCrH4NsdFZ8L8tZ8g3GHn//5qX/+RTawfo+x40fABWPwnf94W2/4HuL99RS5MQ4tYkWAshBMC53VrbR+JhCO6mtX143npVh8SMPOZuPcOSf85hMqn8X0t/nuxWlwB3CSxCC9czeodgY2XJR3+eRG9U+ejB5ubZtCeoE/znb9j4Guz+orB6PVdbUUQIUSokWAshqresZNj4KhxYrG0PPWwBNBp4y7aPpIw85v51hiUR5zCYVP4vzJ/J3SVQi+I91aMe1lYWvLPuOAUGI589EmaereVtHKHfB1rv9eon4bve0O5J6P5Sqa7DLkR1JRvECCGqJ5MR9n4Hm/8LBdnQbrK24sctNtVIyszjy63RLI44i8GkMqSFH1O616OWhwRqcWvf74xh9ppjdA/x5osRYdjqLM03mPxM+PNVrf3Jo65WvQ5oY77xCGFmpbFBjARrIUT1c34PrJ0OFw9BUGfo+wF4NbjpU5Iz8/nyrzMs2q0F6sEt/JjSvS61PWSFBXFnlkScY9aqw3So48nXo1phZ23GcA0QvRVWT4aMeO0PzG6zQFc2m2cIUZFJsBZCiDuRnaL1l+5fCE6+0PstaDz4pm0fKVn5fPXXGRbuPkuBwcTgFv5M6V6XQE8J1OLuLY+M47nlB2kV6M53o1vjaGPmzsz8TG0DpMjvwbO+Vr32v6d8IUSlI8FaCCFuh8kIkfO1DTMKsrQVEbo8DzZOJT4lJSufeduiWbjrLPkGI4NC/ZjSox5BEqhFKVl9IJ7pPx+kub8L88e0wdlWZ+4hwZnNsHoKZCZA+6eg6wtSvRbVhgRrIYS4lbhI+P0ZSNgPgZ201T68G5Z4emphoP6hMFAPDNVaPoK9bt57LcTd+OPIBaYs3U9DX2d+GNMGV3trcw8J8jJgwyzY9wN4hWjrXvu1NPeohChzEqyFEKIkOZe0to99P4CjD/R+E5oMLbHt41J2QWGgjiVXb2Rg85pM6VGPOhKoRRnbfDyRSYv2EezpwOJx4Xg42ph7SJrTG+HXpyDzAnSYBl1nglUFGZsQZUCCtRBC/JvJBPt/0EJ1XsbVtg9b52JPv5RdwNfbo1nwtxaoH2hWk6d61KOutwRqUX62nUxmwsK9BLjZs3hcON7OFaT9Ii8d1r8I+xeBV8PC6nWYuUclRJmQYC2EENeK3we/PwvxkVC7g7bah0+jYk+9fE2gztEb6d+sJlN71KWud8l910KUpV1nUhm7YA8+zrYsGR+Or0sFWlf61J/w6xTISoKOT0PrsdoEYNlVVFQhEqyFEAK0to/N/4W934ODF/R6A5o9WOwv/bScAr7ZHsP8v2PJLjDQr6kvT/WoR30fCdTC/CLPXmL0d3twddCxZFzbirXhUG6aVr0+sFj73N4DfJpAjaZXb571wbICTMIU4i5IsBZCVG8mk/ZLfuOrkHsZ2kyEbi+ArcsNp6bn6PlmRzTf74wlK99Av2a+PNW9Hg1qSKAWFcuhuDRGfvsPDtaWLB7ftuKtRJOwX1sLPvEwXDwMSVFgyNMes7TW1oSv0eya0N0E7NzMO2YhboMEayFE9ZVwQGv7iNsDAW21bZprNL3htPQcPd/ujOH7HTFk5hvo27QGU3vUl0AtKrSjCemM/PYfrCwUlowPr9gtSkYDpJ6GxCPapksXj2iBOzvp6jnO/ldDdo2mWuh2CwILM2zrLkQJJFgLIaqf3Muw+U1tG2Z7D7jvdWj28A2/oNNz9Xy3I4bvdsaQmWegT5MaPNWjHg19i5/EKERFczIxkxHfRGAyqSwaF175fnazkrSAffFwYeg+DCmnQDVqj1s7gk/j69tJvBuBdQVqfxHVigRrIUT1YTLBwaXw5yuQewlaj9O2XrZzve60jDwtUH+7QwvU9zfWAnWjmpUslAgBRCdnMfzrCPIMRhaOCaep/41tTpWKPg+SowoD95GroTs/Q3tcsQD3OtdUtgsDt9PkNLgAACAASURBVFMNmSgpypwEayFE9XDxMKx9Fs7vBv82WtuHb/PrTsnI0zN/ZyzfbI8mI89Ar0Y+TO1Zj8Y1K3kQEdXeudQcHvl6Nxl5ehaMaUNYrSrWr6yqkHZWC9pXKtsXD2vHrpCJkqIcSLAWQlRteemw5S34Z542+em+16H58OvaPjKvBOodMaTn6rmvkQ9Te9SjiZ8EalF1xKflMvzr3aRk5vP9421oE+Ru7iGVvbx0SDx6fTtJ4jEw5muPW1prO0Ne6dmWiZLiHkmwFkJUTaoKh36CDS9DdjK0GgPdXwL7q2EiK9/A/J0xfL1dC9Q9G/owracEalF1JWbkMfzr3SSk5fHNY63oUNfT3EMqf1cmSl48XLgqSTETJV0CCoO2TJQUd0aCtRCi6kk8qrV9nPsb/FpCvw+hZouih7PyDSz4O5avt0eTlqOnR4g303rWr/y9p0LchpSsfB79JoLolGy+GtmSbg28zT2kiiEz8fqgnXgEUk6CatIel4mS4jZIsBZCVB15GbD1bYj4SluHuudr0GJkUZUpO9/Agl2xfL0tmss5erqHeDO1Rz2aB7je7KpCVDmXswsY+V0EJy5m8r/hYfRqXMPcQ6qY9LnaGttFfdtHSpgoWdhCIhMlqz0J1kKIyk9V4fBy2DBLW56r5Wjo8UpR20d2voEfdp1l3rYzXM7R062BF1N71idUArWoxtJz9Tz23T8ciU9nzsOh9G9W09xDqhyKJkpeE7QvHoK0c1fPsff4V9+2TJSsLiRYCyEqt6Qore3j7A6t3aPfh1r7B5BTYGDhrrN8tS2aS9kFdKnvxbSe9WhR1VZEEOIuZebpGTN/D5FnL/Phg80Z3MLf3EOqvHLTtDa0aze5SYq6ZqKkDQR2gHq9oX4vcA8273hFmZBgLYSonPIzYes7EPGl1vvY81UIewwsLMkpMLBo91m++iua1OwCOtf3YmqPerSsLYFaiH/LKTAwbsFedkWn8s6QpjzUupa5h1R1GA2QekoL2Qn74NSf2uegVbDr9YL6vaFWO6lmVxESrIUQlYuqwtEVsH4WZF6AsFHQ4zVw8CC3wKgF6m1nSMkqoFM9T6b1rEfL2tVgWTEh7kGe3sjEhZH8dTKZ1wc2ZlS7QHMPqepKPQOnNsDJ9XB2JxgLwMYZ6nTTqtn17gNHmVBaWUmwFkJUHskn4PdnIWabtrlL3w8hoDW5BUYWR5zly7+iScnKp2NdLVC3CpRALcTtyjcYeXLxfjZGJfJSv4aM6yStCmUuPwuit8Kp9Vo1O/OCdrxmmFbJrtcLfENlmb9KRIK1EKLiMxpg2/uw/QOwdtAmJrZ8nDwjLI44x5d/nSE5M58OdT2Y1rM+rSVQC3FX9EYT0348wNrDF5jRuwFPdqtr7iFVH6qq9Waf3KAF7bi9gAqOPlD3Pi1o1+kGNk7mHqm4idII1lalNRghhLjB5Vj4ZTzE/QNNH4T73ybP2o0lf59jbmGgbl/Hg88faUF4sIe5RytEpaaztOCTh0OxtrLg/fUnyNcbefq++iiydFzZUxTtnTjf5tBlBmSnwOmNWstI1Bo4sAgsdFC7fWE1uzd4yh8+VZFUrIUQZePQMlg7Xbvf/2PyQgbz4z/n+GLrGZIy82kb7M60nvVpK4FaiFJlNKm8uOIwP+09z8TOwczsEyLh2pyMejgfoYXsUxsg+bh23D346iojtTuAlY15xymkFUQIUQHlZcDvM+DQjxAQjmnwPJadseCjP0+SmJFPmyB3nu5Zn3Z1JFALUVZMJpVXfz3Kwt1nGd0+kFcfaCThuqK4fPbqBMiYbdqSftaOENz1am+2k2z6Yw7l0gqiKEoA8ANQAzAB81RV/URRFHfgJyAQiAUeVFX1cuFzXgDGAkbgKVVV1xcebwnMB+yA34GpakVP9kKI2xe3F34Zq2220PUFjtQZz8s/Hmf/uTRa1nbj44dCaRfsIb/ghShjFhYKrw9sjI2VBd/siCHfYOLNQU2wsJD/98zOrTa0Ga/dCnK0cH1qvdafffw37Rzf5oXV7N7aZEiZAFlp3LJirSiKL+Crquo+RVGcgEhgEDAauKSq6juKoswE3FRVfV5RlEbAUqANUBPYCNRXVdWoKMo/wFRgN1qw/lRV1XU3e32pWAtRCZiMsOMj2PI2OPuR1X8u7x9zZeHus7g7WPNCn4YMCfOTQC1EOVNVlQ82nOB/W84wNMyf9/6vGZYSrismVYWkY1ol++R6bW6KagJ7T20Zv3q9oE53sJNdZ0uNIV97B+FSNFyOQWn3RNlXrFVVvQBcKLyfqShKFOAHDAS6Fp62ANgKPF94/EdVVfOBGEVRTgNtFEWJBZxVVd0FoCjKD2gB/abBWghRwaXHwYoJcHYnapOhrPF/ltd/iudSdjoj29Zmeq8GuNjJ5glCmIOiKMzoHYKNlSUf/XmSAqOJjx5sjs5SKqAVjqKAT2Pt1mk65FyC05sKq9l/wMGloFhqG9LU76VVtL0aaM8TJcvPhEsxReGZS9Ha55djtd9flG7jxB2tCqIoSiDQAogAfApDN6qqXlAU5cqK6H5oFekr4gqP6Qvv//t4ca8zAZgAUKuW7CIlRIV1dBWseQpMRhK6fcy0YyH8szeG0ABX5j/ehiZ+LuYeoRACeKpHPWysLHh73XEKDEY+eyQMaysJ1xWavTs0G6bdjAaI33t1AuSfr2g311pXW0YCO4HO1tyjLn+qCjmpJYTnGMhOvv58By9wCyK3ZluOuXuwMdGB3ZedSbCoATxyz8O57WCtKIoj8AswTVXVjJu8pVvcA+pNjt94UFXnAfNAawW53TEKIcpJfhb8MRP2L8To24IvPV7go/VGnG2zeGdIUx5sFSC9nEJUMBO71MHGyoLX1hxj0qJIvhgRhq3O0tzDErfD0gpqtdVuPV/VKq1XJkDuXwR7vgadPQR1uVrNdim2dlk5mUyQmVBy5Tk/45qTFXDxB7dAaNAX3IO0FVjcgshzqsWGMzn8EhnH9gPJmFQIDXBlaCc/+jeriftb9z7U2wrWiqLo0EL1YlVVVxQeTlQUxbewWu0LJBUejwMCrnm6P5BQeNy/mONCiMokYT8sH4t6KZpT9ScyOqY7F2INPNy6Fs/1boCbg7W5RyiEKMHoDkFYW1kya9Vhxv+wl3kjW2FnLeG60nHxh1ZjtJs+F2J3FFaz18PJwg5bnyZaX3b93uDfGiwq+H9no16b+F5SeDbmXz3XQqdNAnUL0lpj3IO0++7BWhX/msq9yaSyJ/YSK3bG8/vhCDLzDfi52vFE17oMDvOjjpdjqX4ZtzN5UUHrob6kquq0a46/D6ReM3nRXVXV5xRFaQws4erkxU1AvcLJi3uAKWitJL8Dn6mq+vvNXl8mLwpRQZhMsOsz2PRfDHYevG03nW/j/Gni58wbg5oSGiATaoSoLJZHxvHc8oO0DnTn29GtcbSR/eKqBFWF5BNXVxk5twtUI9i5Qd2eWiW7bg+tzcQcCrK1kFxceE6P08Z6hc6+sNIcqH28Njy7+N/yD4WYlGxW7otjxf544i7n4mBtSZ+mvgwJ86NtkEex76qWyzrWiqJ0BLYDh9GW2wN4ES0c/wzUAs4Bw1RVvVT4nFnAGMCA1jqyrvB4K64ut7cOmHKr5fYkWAtRAWRcgJUTIeYvTrh3ZUTiCAp0Lsy4P4ThbWrJKgNCVEK/Hkzg6Z8O0Nzfhflj2uBsK5OMq5zcNDizWWsbObVB60VWLCAg/Go127tR6U6AzL18NSxf6XO+8nnWxevPtXMratO4ITw7et/xuNJz9Px2OIEV++KJPHsZRYGOdT0ZGuZPr8Y+2Fvf/A9I2SBGCFH2jq9FXT0ZY0Eu7yuj+SqrI//XMoCZfULwdJSdwoSozP44cpEpS/fR0NeZH8a0wdVeWrmqLJMR4vcVVrPXw8VD2nFn/6t92UGdwdr+5tdRVchKvCY8/6vynJd2/flOvteE58Br7gdpwfoe6Y0m/jqRzIr9cWw8lkSB0UQ9b0eGtvRnUKgfNVxuf0KnBGshRNkpyIENL8Heb4nV1WVs1kR0PiH8d1ATWgea6W1EIUSp23w8kUmL9lHHy5FFY9vgIX8wVw8ZF65Wss9sAX02WNlqq4vU7631LmcnXROeY69+1OdcvY5iCa4B11edr4Rnt8BbB/W7oKoqRxMy+GVfHL8eSCA1uwAPB2sGhNZkaJg/jWs639W+CRKshRBl4+IRTMvHYJFygm+N/fjcYjhT7mvMqHa1sZL1b4WocradTGbCwr0EuNmzeFw43s7VcNm26syQD2d3an3Zp9ZrAfpaVrZaSL4uPBe2bbjWAsvyaSO6mJ7HqgPxrNgXx8nELKwtLejZyJuhYf50ru91z+uzS7AWQpQuVYWILzFteJnLJkemFkzCo1lvZvVtKL9ohajidkenMmb+HnycbVkyPhxfFztzD0mYS8ppbQUoZ18tPDv5mm1b9ZwCAxuOJvLLvjh2nE5BVaFlbTeGhPnRv2lNXOxLL9RLsBZClJ6sJHKXTcDu7BY2Glsw12U6zwxuT/s6nuYemRCinESevcTo7/bg6qBjybi2BLiX/tv4QtyKyaSyOyaVFfviWXf4AtkFRvzd7BgS5s/gFn4EeTqUyetKsBZClAr98T/Q//IfLAoyeV8diXf3J3m8Y7DszCZENXQoLo2R3/6Dg7UlS8a3JbCMQowQ/3YmOYsV++JYtT+B+LRcHG2s6Fe4RF7rQPcy33hMgrUQ4t7o84hb/jz+J+Zz3BTAssDXGDukHzVd5S1gIaqzYwkZPPptBFYWCkvGh1PX28ncQxJV1OXsAn47lMAv++I5cD4NCwU61fNiaEt/7mvoU64bGEmwFkLctaQz+yn4aQz+BdH8ouuP95B36dTQ/9ZPFEJUC6cSMxn+TQQmk8qiceE09HU295BEFVFgMLH1RBIr9sWz6XgieqNKSA0nhob5MzC0ptnm9EiwFkLcsQK9kd3L3qfNiQ/Ixo6/m7xOr8GjsLGq4NvdCiHKXXRyFsO/jiDPYGTR2HCa+LmYe0iiklJVlUNx6azYF8evBxO4nKPH09GagaF+DA3zp1FN8//hJsFaCHFH/jl6EsPKJ2lv+Icjdq1xG/4NfgGB5h6WEKICO5eawyNf7yYjT88PY9rQota9b+ohqo+EtNzCJfLiOZ2UhbWVBb0a+TA0zJ9O9Twr1BKuEqyFELclKSOPn39eyLDzb+CmZBEb+hz1B8ww2/JJQojKJT4tlxFf7yY5M5/vH29DmyDZJEqULDvfwB9HLrJifxx/n0lFVaF1oBtDw/zp09QXF7vyWff6TkmwFkLclMFoYuHO06ib/ssY5VdS7YJwHD4fm4BQcw9NCFHJJGbkMfzr3SSk5TG2YxA9GnrT3N+1zFdqEJWD0aSy60wqK/bFse7IRXL1Rmq52zMkzI/BLfyo7VHxV5eRYC2EKNGe2Et89csfTE1/l6YWsWQ0GYXzgHfLZHtZIUT1kJKVz/SfD7LjVDImFTwdrenWwJseDb3pWM8LRxsrcw9RlLPTSZn8si+eVfvjuZCeh5OtFf2b1WRomB8ta7vd1dbi5iLBWghxg+TMfN75PQrLQ4uYrfsBS2s7dIM/R2n4gLmHJoSoItJyCvjrZDKbopLYeiKJjDwD1pYWhAe70yPEmx4NfWRzmSosNSufNQcTWLE/nkNx6VhaKHSp78WQMD96NvTBVlc5J8NLsBZCFDGaVBZHnOWr9ZG8ZPqKPhYRGGt3wnLoPHCuae7hCSGqKL3RROTZy2yKSmTT8SSik7MBqO/jSI+GPvQI8aZFLTcspWWkUss3GNlyPIlf9sWz5XgSBpNKI19nhrb0Z0Dzmng52Zh7iPdMgrUQAoD95y7z8uojOCRE8IX9XNzVNJTuL0P7p2SCohCiXMWkZGshOyqJPbGXMJhU3Ox1dGvgTfeG3nSu74WzbcWcvCaup6oq+8+nsWJfHGsOXiA9V4+Xkw2DW2h901VtbXMJ1kJUc5ezC3j3j+Ms3xPDLPtVjDatBPcglKHfgl+YuYcnhKjm0nP1bDuZzObjSWw5kURajh4rC4U2Qe5F1WzZMr1iydMbOZqQzt+nU1m5P57olGxsdRb0blyDIWH+dKjjUaGWyCtNEqyFqKZMJpWf9p7n3T+O45YXzyK3efhlH4MWj8L974KNo7mHKIQQ1zGaVPadu8ymqCQ2RSVyKikLgDpeDkUhu2Vttyob2ioio0nlTHIWB86nceB8GgfPp3HiYiYGk5YNw4PcC5fIq4FTNXiXQYK1ENXQ4bh0Xlp9hIPnL/OMz36ezJmLhYUVPDAHmgwx9/CEEOK2nEvNYdPxRDYfT2J3dCp6o4qzrRVdC1cZ6VrfGxf7qh/myouqqlxIz+Pg+TQOxGkh+nBcOtkFRgCcbK0IDXClub8rzQNcaR7ggreTebYWNxcJ1kJUI+k5ej7YcIJFEWepZa9nUY2fCIj/HWq1hyHzwDXA3EMUQoi7kpmnZ8epFDYdT2LL8SRSswuwtFBoVduNHg21VUaCPR0q1dJt5paeo+dQvBagD5xP52BcGsmZ+QBYW1rQsKYzLQoDdHN/VwI9HKr9muQSrIWoBlRV5Zd98bz9exSXcwqY1TSTxxPfxCIjHrq+AJ2mg0XlXNpICCH+zWhSORiXVjQB8vjFTAACPeyLWkZaB7mjk5aRIvkGI8cSMjh4Po2DcekcPJ9GdEp20eN1vBxoHuBaVJEO8XXCxkp+b/ybBGshqrioCxm8svoIe2Iv0zLAiS9qbcFn3yfg4g9Dv4GANuYeohBClKm4yzlsOZ7Exqgkdp1JpcBowsnGis4NvOgR4k23Bt64OVibe5jlxmRSiU7JLgzRWkX62IUM9EYtz3k72WgBujBIN/V3kVVYbpMEayGqqMw8PR//eYoFu2JxtrXi9S7O9D/9Ksr53dDsIej7AdhWrWWOhBDiVrLzDew8ncKmqCQ2n0giOTMfCwXCarlp1eyG3tTzdqxSLSOJGXlFEwsPxqVx6Hw6mfkGABxtrGjq51IUokMDXKnhUr36okuTBGshqhhVVfn1YAJvrI0iJSufR9rUYlatYzhsmAGqCfp9CM0fMvcwhRDC7EwmlcPx6Ww6rq0ycjQhA4AAdzt6hPjQPcSb8GD3StXykJmn53BcetHkwoPn07mYkQeAlYVCQ1/nop7o0ABXgr0cZeOdUiTBWogq5FRiJq+sPsqu6FSa+rnwVt9Amh5+Cw4sBv/WMORrcA8y9zCFEKJCupiex+bCkL3jdAr5BhMO1pZ0qudFj4bedAvxxtOx4uwOWGAwceJiJgfOXy6aXHgmOYsrsSzQw76opaN5gCuNfJ0r7VbhlYUEayGqgOx8A59uPsW322Owt7Zkxv0hDPdLxnLFOEg7C52ehS7PgaX0yAkhxO3ILTDy9xltlZHNUUlczMhDUSA0wJUeIdoqIyE1nMqtZURVVWJTcwpX6NBaOo4mZFBgMAHg4WB9XYhu7u+Cq3316RuvKCRYC1GJqarKuiMX+e9vx7iQnsewlv7M7F0Pj4NfwpY3wbEGDP0aarc391CFEKLSUlWVowkZRdXsg3HpAPi52tE9RNtmvV2wR6lWg5Mz84t6og+cT+NQXDrpuXoA7HSWNPV3uWbNaBf8XO2qVF94ZSXBWohKKiYlm1dWH2H7qRRCajjxxqAmtHLLhZUTIXY7NBqkbfhi52buoQohRJWSlJnHluNJbIpKYvupFHL1Rux0lnSs50mPEG+6h3jj7Xz7EwCz8w0ciU8vCtEHz6cTn5YLgKWFQn0fJ0IDXIoq0nW9HGV3yQpKgrUQlYyqqiz4O5a3fj+OjZUF03vVZ2Tb2lid+A1+nQJGPfR9D0JHgFQvhBCiTOXpjeyOTtVWGTmeVBSIm/m70CNEW2WkcU3nomqy3mjiZGImB8+nF1WkTyZmUrgDOAHudkUTC5sHuNK4pjP21lbm+vLEHZJgLUQlUmAw8eqvR1j6z3l6hHjz9pCmeNsaYf2LEDkfaraAod+CRx1zD1UIIaodVVU5kZjJpiitZWT/+TRUFXycbWgX7EHc5VyOJKSTp9f6ot3sdYX90FqQbubvgkcFmhwp7pwEayEqiUvZBUxaFMk/MZd4omsdnu3VAIvEQ7B8LKSehg5TodsssJLJKkIIURGkZOWz9UQym6IS2RN7mdoe9kU90aEBrtRyt5e+6CqmNIK1vD8hRBk7cTGTsQv2kJSZzycPhzKwmS/s/hw2zgYHTxi1CoK7mnuYQgghruHpaMP/tfTn/1r6m3soohKRYC1EGfrzWCLTftyPg40VP09sR6iPNSwbBVFrIKQ/DPgM7N3NPUwhhBBClAIJ1kKUAVVVmfvXGd5ff4Kmfi7MG9mKGhZpMH8QJByAXm9CuydlgqIQQghRhUiwFqKU5emNzPzlEKsOJNC/mS/v/19z7C4dgyUPQW4aPLwEQvqae5hCCCGEKGUSrIUoRUkZeUxYGMmB82k8c199Jnevi3JqAywfAzbOMGYd+DY39zCFEEIIUQYkWAtRSg7HpTP+h71k5On58tGW3N/YByK+gvUvgE8TGP4TONc09zCFEEIIUUZuufWPoijfKYqSpCjKkWuONVcUZZeiKIcVRVmjKIpz4XGdoigLCo9HKYrywjXPaVl4/LSiKJ8qskaNqEJ+O5TAsK/+xtJCYfmk9tzf0BN+fxb+eB7q94Exf0ioFkIIIaq429lTcz5w/7+OfQPMVFW1KbASmFF4fBhgU3i8JTBRUZTAwsfmAhOAeoW3f19TiErHZFL5aMMJJi/ZT5OaLqye3IFG7sDSh2DPN9B+Cjy0EKwdzD1UIYQQQpSxWwZrVVW3AZf+dbgBsK3w/p/A0CunAw6KolgBdkABkKEoii/grKrqLlXbkeYHYFApjF8Is8kpMPDE4n18uvk0w1r6s3h8OJ6GRPiuN0RvhQc+gV5vgIWluYcqhBBCiHJwtz3WR4ABwGq0KnVA4fHlwEDgAmAPPK2q6iVFUVoBcdc8Pw7wK+niiqJMQKtuU6tWrbscohBlJz4tl3EL9nLiYgYv9WvI2I5BKPGRsPRhMBTAo7/Ipi9CCCFENXM7rSDFGQM8qShKJOCEVpkGaAMYgZpAEPCMoijBQHH91CXupa6q6jxVVVupqtrKy8vrLocoRNmIPHuJgZ/vIO5SDt+Obs24TsEoR1fC/H5ay8e4PyVUCyGEENXQXVWsVVU9DvQCUBSlPtCv8KHhwB+qquqBJEVRdgKtgO3AtXuC+gMJdztoIcxl2d7zzFp5hJqutvw4oRV1vRxh2wew+b8Q0BYeXqxtUy6EEEKIaueuKtaKongXfrQAXgK+LHzoHNBd0TgAbYHjqqpeADIVRWlbuBrIKLQ2EiEqBaNJ5c21x5ix/BCtg9xY9WQH6rpbw6ontFDddBiMWi2hWgghhKjGblmxVhRlKdAV8FQUJQ54FXBUFOXJwlNWAN8X3v9f4f0jaO0f36uqeqjwsf+grTBiB6wrvAlR4WXk6Xlq6X62nkhmVLvavNy/Ebr8NFj4KJzdCV1fhC7PyfbkQgghRDV3y2CtquojJTz0STHnZqFNZizuOnuBJnc0OiHMLDYlm3E/7CU2JZs3BzdhRHhtSDkNS4ZBehwM+QaaFfsjL4QQQohqRnZeFKIEf59O4T+L96EosHBsOO3qeEDMdvjpUW0JvcfWQK225h6mEEIIISoICdZCFGPhrlheW3OMYE8HvnmsFbU9HGD/IlgzDdyDte3J3YPMPUwhhBBCVCASrIW4ht5oYvaaoyzafY4eId7MeTgUJ2tL2DgbdnykLaM3bAHYuZp7qEIIIYSoYCRYC1HocnYBTyzex67oVCZ2Cea53iFYGnJh+Vg4thpajoa+H4ClztxDFUIIIUQFJMFaCOBUYiZjF+zlYnoeHw5rztCW/pCZqO2kmLAfer0J7Z6UlT+EEEIIUSIJ1qLa23I8iSlL92Ors2TphLa0rO0GiUdhyUOQk6pt+hLS79YXEkIIIUS1JsFaVFuqqvL19mjeXnecRr7OfD2qFTVd7eDUn7DscbBxhMfXQc1Qcw9VCCGEEJWABGtRLeUbjLy44gi/7Iujb9MafDCsOfbWVhAxD/54HnyaaCt/ONc091CFEEIIUUlIsBbVTlJmHpMWRrLvXBrTetbjqe71sMAEvz8H/3wFDfrCkK+1irUQQgghxG2SYC2qlSPx6Uz4YS+Xcgr4YkQYfZv6Qn6mtvLHqfXQbjLc97q2AYwQQgghxB2QYC2qjXWHLzD954O42utYPqk9TfxcIO28tvJHUhT0/xhajTH3MIUQQghRSUmwFlWeqqp8uuk0H288SYtarnw1siXeTrYQHwlLHwF9Ljy6HOp0N/dQhRBCCFGJSbAWVVpugZFnlx1k7eELDAnz463BTbHVWcKxX2HFBHD0glG/gneIuYcqhBBCiEpOgrWosi6k5zL+h70cTcjghT4hTOgcjAKw42PY+Br4t4GHl2jhWgghhBDiHkmwFlXSvnOXmbgwktwCI9+MakWPhj5gKIC1T8P+RdBkKAz8AnS25h6qEEIIIaoICdaiylmxL46ZKw5Tw/n/27vv+Kiq9I/jnychEHqRIh1UiiJNAsSyiu7K2hVUFCsisPZ1/Ym69lV3Ze1iR7CgGFcFRBAEVBRQYCkiRekgICi99+T5/XEvyxAJYBhyM8n3/XrllZl7z1yew2Qy35w5955U+nVpTf0qpWHrWvjP1bBoDJx2N7T5u5YnFxERkbhSsJYCIzPLeWL4LF77egHpR1Xg5StbUKFkUVg9H97rAOsWQ7te0PSyqEsVERGRAkjBWgqEjdt2cvv7U/li1gqubF2Lhy9oREpyEvz0Lbx/BWDBSYq1T4y6VBERESmgFKwl4S1evYUufScyf+VmHr2wEVefWCfY8f37MOgWKF8HrvwAKhwVZZkiIiJSwClYS0IbN381N/WbTJZD386tOPmYipCVBV/9C0Y/CXVPSHICrwAAIABJREFUhQ59oXj5qEsVERGRAk7BWhLWexMW8+CgGdQ+ogS9r21J3Yolg8VePr4JZg6A5lcHqykmp0RdqoiIiBQCCtaScHZlZvHokB94e9xPnFa/Ei9c0ZwyqSmwaUUwn3rpJDjzETjpNl35Q0RERPKMgrUklHVbdnDze1P4Zt5qupxSl7+fcyzJSQYrfoR+HWDzSrjsHTj2/KhLFRERkUJGwVoSxrwVm+jy9kR+XreVJy5pQoe0muGOz+HD6yClBFw3FKqfEG2hIiIiUigpWEtC+Gr2Cm7N+I6iyUlkdE0nrU6FYMfE3jD0Lqh8HFzxPpStEW2hIiIiUmgpWEu+5u70GbuQfw39kQZHluH1a1pQo3wJyMqEEffD+Jeh3p/hkj5QrHTU5YqIiEghpmAt+db2XZk88PEMPpi0lD83qsIzHZpRslgR2L4J+l8Pcz6D9Jug7WOQlBx1uSIiIlLIKVhLvrRq03ZufHcyExet5bYzjuH2P9UnKclg/c+QcRn8+gOc8xS06hp1qSIiIiKAgrXkQz8s20DXvpNYtWk7PTs254Km1YIdy76D9y6HHZvhig+g3p+iLVREREQkhoK15CvDZ/7C3/4zldKpRfjwhhNpUqNcsOPHITCgK5SoCNcPhCrHRVuoiIiISDYK1pIvuDsvjZrHUyPm0LRGWXpdk0aVMqngDt++ACMfhOotoGMGlKocdbkiIiIiv6FgLZHbtjOT7h9NY/D3y7iwWTX+fXETUlOSIXMnfHoHTOkLjdrBRa9ASvGoyxURERHZJwVridTi1Vu4JWMK039ez11nNeDG047GzGDrWvjgWlj4NZzaHdrcC0lJUZcrIiIikiMFa4lEZpbz5jcLeWrEbIokJfHaVS1o2+jIYOeaBfDeZbBmIVz0KjTrGG2xIiIiIgdBwVry3I/LN3BP/2l8v3Q9f2xYmcfaHU/VsuEUj8Xj4f0rwLPgmkFQ5+RoixURERE5SAf8bN3M3jCzFWY2I2ZbUzMbZ2bTzWywmZWJ2dck3Dcz3J8abm8R3p9nZj3NzA5PlyS/2rYzk6eGz+b8F8aydO1WenZsTu9r0/aE6mkfwNvnQ/Hy0OULhWoRERFJKAczafUt4Kxs23oD97h7Y2Ag0B3AzIoA7wI3uHsjoA2wM3zMK0A3oF74lf2YUoBNXLSGc3uO4cVR87igaTU+v+M0LmhaLZhP7Q6jHg8up1ezNVw/Eo44OuqSRURERH6XA04FcffRZlYn2+YGwOjw9khgOPAA0BaY5u7fh49dDWBmVYEy7j4uvN8XuAgYduhdkPxs47adPPHZbN4Z/xPVyxXnreta0qZBzOXydmyBT26FGR9B86vg3GehSNHoChYRERHJpdzOsZ4BXAAMAi4Faobb6wNuZsOBSsD77v4EUB1YGvP4peG2fTKzbgSj29SqVSuXJUrUvpz1K/cNnMEvG7bR6aQ6dP9zA0oWi/mRWzgmCNVrF8KfHoaTbwfNEBIREZEEldtg3RnoaWYPAp8AO2KOdwrQEtgCfGFmk4EN+ziG53Rwd+8F9AJIS0vLsZ3kT6s3beeRIT8waOoy6lUuxUc3nESL2uX3NNi+EUY+BJP6QPk6cO0QqPuHyOoVERERiYdcBWt3n0Uw7QMzqw+cG+5aCnzt7qvCfUOBEwjmXdeIOUQNYFkua5Z8yt0ZNHUZ/xg8k03bd/HXP9bjptOPpliR5D2N5n0Og2+H9Ush/WY44z4oWjK6okVERETiJFfB2swqu/sKM0sC7gdeDXcNB+4ysxIEo9inAc+6+3Iz22hm6cAE4BrghUMvX/KLn9dt5b6B0/lq9kqa1SzHE5c0oX6V0nsabF0Lw++Hqe9Cxfpw/Qio2Sq6gkVERETi7IDB2swyCK7uUdHMlgIPAaXM7OawyQDgTQB3X2tmzwATCaZ6DHX3T8N2NxJcYaQ4wUmLOnGxAMjKct4Z/xNPfDaLLIcHzzuOa0+qQ3JSzFzpWUNhyN9g80r4w//BqXdBSmp0RYuIiIgcBuaev6cwp6Wl+aRJk6IuQ/Zh3oqN3N1/OpN/Wssf6lXkX+0aU7NCiT0NNq+CYXcHV/yocjxc+BJUaxZdwSIiIiI5MLPJ7p52KMfQyovyu+3YlcWrX8/nxS/nUaJYMk9f2pT2J1Tnf2v+uMPMATC0O2zbAKffF1zxQ5fRExERkQJMwVp+l6lL1nFP/2nM+mUj5zWpykPnN6JS6WJ7Gmz8BT79P5g1BKqdEIxSVzkuuoJFRERE8oiCtRyULTt28fSIObz5zUIql07l9WvSOPO4KnsauMP3GfDZPbBzG5z5SHDVj2T9iImIiEjhoNQjBzR27ir+PnAaS9Zs5crWtbj77IaUSU3Z02DdEhhye3ApvZrpcOGLULFedAWLiIiIREDBWnK0bssOHvv0Rz6avJSjKpbkP93SaX3UEXsaZGXBlLdgxIPgWXD2k9CyCyQlRVaziIiISFQUrOU33J2h03/hoU9msnbLDm5qczS3/bEeqSkxC72sWRgsR75oDNQ9FS54IVhFUURERKSQUrCWvfy6YRv3fzyDkT/8yvHVy/B255Y0qlZ2T4OsTPhvL/jiEUgqAuc/DydcC2Y5H1RERESkEFCwFiBY6OX9iUt4fOiP7MjM4u9nN+T6U+pSJDlmWsfKOfDJLbBkAtRrC+c9B2WrR1e0iIiISD6iYC0sWrWZewZMY/yCNaQfVYEe7ZtQp2LJPQ0yd8G4F2DU45BSHNr1giYdNEotIiIiEkPBuhDblZlF77ELeXbkHIoWSaJH+8Zc1rLmnoVeAH6ZAYNuhuVT4djz4ZynoXSVnA8qIiIiUkgpWBdSM5et5+7+05jx8wb+3KgKj1x4PFXKpO5psGsHjHkaxjwFxcvDpW9Do4uiK1hEREQkn1OwLmS27czk+S/m0mv0AsqXKMorV57A2Y2r7t3o5ykw6BZYMRMad4CzekDJI/Z9QBEREREBFKwLlQkLVnPPgOksXLWZDmk1uO+c4yhbImahl51b4ase8G1PKFUFOr4PDc6OrmARERGRBKJgXQhs2LaTHsNm8d6ExdSqUIJ+XVpz8jEV9260eHwwSr16LjS/Gto+BsXLRVOwiIiISAJSsC7gRv7wKw98PIMVG7fR5ZS63NG2PiWKxjztOzbDF4/ChFehbE24eiAcfUZ0BYuIiIgkKAXrAmrlxu08PHgmn05bTsMjS/Pa1S1oWjPbCPSCr4PVE9f9BK26wR8fgmKloilYREREJMEpWBcw7k7/KT/z6JAf2Lojkzvb1qfbqUdTtEjMQi/b1sPIB2HyW1DhKOg0FOqcHFnNIiIiIgWBgnUBsmTNFu4dOJ0xc1eRVrs8PS5uwjGVs41Azx0Jg/8KG5fDSbdCm3uhaIloChYREREpQBSsC4DMLOetbxfx1PDZJBk8emEjrmxdm6SkmIVetqyB4ffC9xlQqSF06As10qIrWkRERKSAUbBOcLN/2cjd/acxdck6Tm9QicfaNaZ6ueJ7N/pxMAy5A7ashlO7B19FikVTsIiIiEgBpWCdoLbvyuSlUfN55at5lE5N4fnLm3FB02p7L0e+aSUM6w4zB8KRjeGqj6Bq0+iKFhERESnAFKwT0OSf1nJP/2nMXbGJi5pV48HzG1GhZNE9DdxhRn8Y2h12bIIzHoCT/wrJKTkfVEREREQOiYJ1Atm8fRdPDp/N2+MWUbVMKm9e15LTG1Teu9GG5fDpHTB7KFRPgwtfgsoNI6lXREREpDBRsE4QX89Zyb0DprNs/VauSa9N97MaUqpYzNPnDt+9C8Pvg8zt0PafkH4jJCVHV7SIiIhIIaJgnc+t3byDR4f8wIDvfuaYyqX46IYTaVG7wt6N1i2GT26DBaOg9slwwQtwxNHRFCwiIiJSSClY51PuzuBpy/nHJzNZv3Unt51xDDefcQzFisSMQGdlwaQ+8PnDwYj1OU9B2vWQlJTjcUVERETk8FCwzoeWr9/K/QNn8MWsFTStWY5+Fzem4ZFl9m60en6wHPlP38BRp8P5z0P52tEULCIiIiIK1vmJu9NvwmJ6DJtFZpZz/7nHct3JdUmOXeglKxPGvwJfPgbJRYOTE5tdCbGX2RMRERGRPKdgnU+4O//89Ed6j13IKcdU5PH2jalZIdtS4ytmwaCb4edJUP9sOO9ZKFM1moJFREREZC8K1vmAu/P4sFn0HruQa0+szcMXNNp7oZfMnfDN8/D1v6FoKbi4Dxx/sUapRURERPIRBeuIuTs9PptFr9ELuDp9H6F6+TQYdBP8Mh0atYOzn4RSlaIrWERERET2ScE6Qu7Ok8Nn89rXC7gqvRaPXBgTqndth9FPwdhnoHgF6PAOHHdBtAWLiIiISI4UrCPi7jw9Yg4vfzWfK1rX4pELjt8TqpdOCuZSr5wFTTvCn/8FJSrs/4AiIiIiEikF64g8+/lcXhw1j8tb1uSxC48nKclg5zYY9RiMewlKV4UrPoT6baMuVUREREQOgoJ1BJ77fA49v5hLh7Qa/Ktd4yBU//oD9O8CK2ZCi05w5iOQWjbqUkVERETkIB1wiT4ze8PMVpjZjJhtTc1snJlNN7PBZlYm22NqmdkmM7szZluLsP08M+tpVjgvadHzi7k89/lcLmlRgx7tm5BkwITXoFcb2LwCrvwoWOxFoVpEREQkoRzM2tdvAWdl29YbuMfdGwMDge7Z9j8LDMu27RWgG1Av/Mp+zALvxS/n8szIOVx8Qg3+fXETkrashH6XwrC74KjT4MZxUO/MqMsUERERkVw4YLB299HAmmybGwCjw9sjgYt37zCzi4AFwMyYbVWBMu4+zt0d6AtcdGilJ5aXRs3jqRFzaN+8Ok9c0oTkeSPg5RNh0Rg45ym44gNdRk9EREQkgR3MiPW+zAB2X/vtUqAmgJmVBO4G/pGtfXVgacz9peG2fTKzbmY2ycwmrVy5Mpcl5h+vfDWfJ4fP5sJm1XjyovokD+sO73WA0kdCt6+gVVct9iIiIiKS4HIbrDsDN5vZZKA0sCPc/g/gWXfflK39vlKj53Rwd+/l7mnunlapUmKP4r729Xz+/dksLmhajadPTSa59+kw8XVIvxm6fAGVj426RBERERGJg1xdFcTdZwFtAcysPnBuuKs1cImZPQGUA7LMbBvQH6gRc4gawLLcFp0oeo9ZwOPDZnF+4yo8W/tbkvs8DMXLw1X94Zg/RV2eiIiIiMRRroK1mVV29xVmlgTcD7wK4O5/iGnzMLDJ3V8M7280s3RgAnAN8MIh1p6v9Rm7kMc+/ZGOxxbln5n/JGnEl9DgHLjgBShZMeryRERERCTODhiszSwDaANUNLOlwENAKTO7OWwyAHjzIP6tGwmuMFKc4Ioh2a8aUmC8+c1CHh3yA3fXnc8Nvz6H7dgC5z4DaZ01l1pERESkgLLgIh35V1pamk+aNCnqMg7a298u4vFPpvBa5QGctmEwHNkYLu4DlRpEXZqIiIiI5MDMJrt72qEcQysvxtE74xbxweBPGVX6VapuWAwn3QpnPABFikVdmoiIiIgcZgrWcfLuuIUsHvIEg4p9QHJqJbj8Yzj69KjLEhEREZE8omAdBwO/nkidz//KVSkzyWxwHnbhC1CiQtRliYiIiEgeUrA+RGM+eZM2k++nRJFd7DznOVLSOukERREREZFCSME6t3ZsZv47t/GHJR+xsFg9SnR+l5QjG0ZdlYiIiIhERME6N5Z9x8Z+nai76ScGl72cM296jmKpxaOuSkREREQilNslzQunrEwY+yxZr/+JTZs20KPyvznz1pdJVagWERERKfQ0Yn2w1v8MA/8Ci8bwWWYrBtW8i+evO4PUlOSoKxMRERGRfEDB+mDM/BgG/5VdO7dz785uLK3dnj6dWilUi4iIiMj/KFjvz/ZNMOxumPoua8s1pv366ziybiPe6NSS4kUVqkVERERkDwXrnCydDAO6wJqFzGnwF86bdjLN61SmT6c0hWoRERER+Q2dvJhdViaMfgreaAu7djDu1L6cPb0NzWpX5o1OLSlRVH+LiIiIiMhvKSXGWrcEBnSDxd9Co/aMPOpubui/gBNqlePN61pSspj+u0RERERk35QUd5vRHwb/DTwTLnqVz5LbcEvGdzSrWY43r2ulUC0iIiIi+6W0uG0DDLsLvs+AGi2hfS9GLC/BLf2m0KRGWd66riWlFKpFRERE5AAKd2JcMjE4QXHdYjjtbji1OyNnr+Hm9yZzfPWyvNW5FaVTU6KuUkREREQSQOEM1pm7YMzT8PW/oUx1uG4Y1Ernix9/5aZ+kzmuWln6Xt+KMgrVIiIiInKQCl+wXvtTcILikvHQ+FI492lILcuXs37lxnencGzVMvTtrFAtIiIiIr9P4QrW0z6AT/8vuN3+dWjSAYBRs1dwwztTqH9kKd7p3JqyxRWqRUREROT3KRzBett6+PROmP4B1GwN7XtB+ToAfD1nJX95ZzL1qpTi3etbU7aEQrWIiIiI/H4FP1gvHg8DusL6n6HNvfCH/4PkoNuj56yka99JHFOpFP26tKZciaIRFysiIiIiiargBuvMXTD6CRj9JJStCZ0/g5qt/rd77NxVdO07iaMVqkVEREQkDgpmsF6zMBilXjoRmnaEs5+A1DL/2/3tvFVc//ZE6lYsSb8urSlfUqFaRERERA5NwQrW7vD9+zC0O1gSXNwHGl+yV5Nx81fT+e2J1DkiCNUVFKpFREREJA4KTrDeug6G/A1mDoBaJ0H716Bcrb2ajF+wms5vTaRm+RL069qaI0oVi6hYERERESloCkawXvQNDPwLbFgGZzwAp/wNkpL3ajJhwWque3Mi1csX572u6VRUqBYRERGROErsYJ25E77qAWOfgXK14fqRUKPFb5pNXLSG696aSLVyqbzXtTWVSitUi4iIiEh8JW6wXj0f+neBZVOg+VVwVg8oVvo3zSb/tIZOb/yXI8ukktE1ncqlUyMoVkREREQKusQL1u4wtR8MvSu4HvWlb0GjdvtsOvmntVz7xkQql0klo1s6lcsoVIuIiIjI4ZFYwXrLmuAExR8+hjp/gHavQtka+2w6ZfFarn3jv1QsVZSMrulUUagWERERkcMocYL1wjHBCYqbfoU/PQwn3fabExR3m7pkHdf2+S9HlCpKRrd0jiyrUC0iIiIih1f+D9buMPIh+OZ5OOJo6PI5VGueY/NpS9dxdZ8JlC8ZjFRXLVs8D4sVERERkcIq/wfrVXPgm+fghGuCExSLlsyx6fSl67mq9wTKlUgho1s61copVIuIiIhI3sj/wTpzB3T4EI67YL/NZvy8nqv6TKBM8RQyuqZTXaFaRERERPJQUtQFHFClhgcVqq/sPYFSxYqQ0TWdGuVL5FFxIiIiIiKB/B+sk1P2u/uHZRu4qs8EShZN5v1u6dSsoFAtIiIiInnvgMHazN4wsxVmNiNmW1MzG2dm081ssJmVCbefaWaTw+2TzeyMmMe0CLfPM7OeZmaHWvyPyzdwZe/xFE9JJkOhWkREREQidDAj1m8BZ2Xb1hu4x90bAwOB7uH2VcD54fZrgXdiHvMK0A2oF35lP+bvMvuXjVzZewLFiiST0TWd2kfkfFKjiIiIiMjhdsBg7e6jgTXZNjcARoe3RwIXh22/c/dl4faZQKqZFTOzqkAZdx/n7g70BS7KbdFzft3IFa+PJyXZyOiWTp2KCtUiIiIiEq3czrGeAew+o/BSoOY+2lwMfOfu24HqwNKYfUvDbftkZt3MbJKZTVq5cuVe++aGoTo5ycjomk5dhWoRERERyQdyG6w7Azeb2WSgNLAjdqeZNQL+Dfxl96Z9HMNzOri793L3NHdPq1Sp0v+2z1uxiY6vT8DMeK9rOkdVKpXL8kVERERE4itX17F291lAWwAzqw+cu3ufmdUgmHd9jbvPDzcvBWrEHKIGsIzfYf7KTXR8fTwAGV1bc0xlhWoRERERyT9yNWJtZpXD70nA/cCr4f1ywKfA3939m93t3X05sNHM0sOrgVwDDDrYf2/Byk107DUedw9DdenclC0iIiIictgczOX2MoBxQAMzW2pm1wMdzWwOMItg5PnNsPktwDHAA2Y2NfyqHO67keBqIvOA+cCwgylw+64sOr4+nsws572u6dSrolAtIiIiIvmPBRfpyL9KVW/g9W94kfe6tqbhkWWiLkdERERECiAzm+zuaYdyjHy/8mIWTr8uCtUiIiIikr/l+2B9VMWSHFtVoVpERERE8rd8H6xTU5KjLkFERERE5IDyfbAWEREREUkECtYiIiIiInGgYC0iIiIiEgcK1iIiIiIicaBgLSIiIiISBwrWIiIiIiJxoGAtIiIiIhIHCtYiIiIiInGgYC0iIiIiEgcK1iIiIiIicWDuHnUN+2VmG4HZUdeRx8oC66MuIg8Vtv6C+lxYqM+Fg/pcOBS2Phe2/gLUc/eyh3KAIvGq5DCa7e5pUReRl8ysl7t3i7qOvFLY+gvqc2GhPhcO6nPhUNj6XNj6C0GfD/UYmgqSPw2OuoA8Vtj6C+pzYaE+Fw7qc+FQ2Ppc2PoLcehzIkwFmVTYRqxFREREJPEkwoj1IQ/Li4iIiIgcbvl+xFpEREREJBEkwoh1gWNmmWY2Nearzn7afmVmCT0VxszczN6JuV/EzFaa2ZAo68oLZtYu7H/DqGs5nArzcwxgZpuiriEKB+p3Qfj9BYXndRzLzO4zs5lmNi18n2oddU2Hm5nVMLNBZjbXzOab2fNmVnQ/7W83sxJ5WWM8hT/TT8fcv9PMHo6wpMMuJn/NNLPvzewOM4trFlawjsZWd28W87Uo6oIOs83A8WZWPLx/JvDz7zmAmSXCFWz2pSMwFrj89zzIzJIPTzmHzSE/xyL5WK5ex4nKzE4EzgNOcPcmwJ+AJdFWdXiZmQEDgI/dvR5QHygF/HM/D7sdSNhgDWwH2ptZxagLyUO781cjgvepc4CH4vkP5JtgXVhHfHYzsxZm9rWZTTaz4WZWNWb3VWb2rZnNMLNWkRV5aIYB54a3OwIZu3eYWauwf9+F3xuE2zuZ2YdmNhgYkfclHxozKwWcDFxP+IZsZm3MbLSZDTSzH8zs1d1/LZvZJjN7xMwmACdGV3mu5eY5HmNmzWLafWNmTfK06jgJn9shMfdfNLNO4e1FZvYPM5tiZtML0sjn/vpdEOzndZzTc32Omc0ys7Fm1jNBP7WpCqxy9+0A7r7K3Zfl9D4VfjLxXIK/T50BbHP3NwHcPRP4G9DZzEqa2VPha3eamd1qZrcB1YBRZjYqwroPxS6C89j+ln2HmdU2sy/C/n5hZrXMrGz4u2z3e1YJM1tiZil5XXg8uPsKoBtwiwWSzexJM5sY9vsvu9ua2V3h8/+9mfXY33HzTbAuZIrbnmkgA8MfyheAS9y9BfAGe/+VXNLdTwJuCvcloveBy80sFWgCTIjZNws41d2bAw8C/4rZdyJwrbufkWeVxs9FwGfuPgdYY2YnhNtbAf8HNAaOBtqH20sCM9y9tbuPzfNqD11unuPeQCcAM6sPFHP3aXlWcd5a5e4nAK8Ad0ZdjBy0nF7HvxH+7L8GnO3upwCV8qjGeBsB1DSzOWb2spmdVgjepxoBk2M3uPsGYDHQBagLNA9H8Pu5e09gGXC6u5+e18XG0UvAlWaWfVGUF4G+u/sL9HT39cD3wGlhm/OB4e6+M8+qjTN3X0CQhSsT/PG83t1bAi2BrmZW18zOJvg90NrdmwJP7O+Y+SpYm1mp8C+j3aM6F4bb65jZj2b2ugXzYkbYno+cE1HsVJB2QAPgeGCkmU0F7gdqxLTPAHD30UAZMyuX5xUfojAs1SEYyRyabXdZ4EMzmwE8S/ALbreR7r4mT4qMv44EYZPwe8fw9n/dfUE4IpIBnBJuzwT6522J8ZPL5/hD4LzwTbsz8FaeFBuNAeH3yQT/T5IYcnod70tDYIG7LwzvZ+ynbb7l7puAFgSjeSuB/wB/oWC/Txmwr6s5GHAq8Kq77wJI4Pek3wj/eOgL3JZt14nAe+Htd9jzPvUf4LLw9uXh/URn4fe2wDXhz/cE4AigHsFUqDfdfQsc+PnPb/NWtwHt3H2DBXN+xpvZJ+G+ekBHd+9qZh8AFwPvRlVonBkw091z+vg/+4s9US/l8gnwFNCG4Ad2t0eBUe7ezoITOb+K2bc5j2qLKzM7guCjxePNzIFkgudtKDk/n9vCsJ3Iftdz7O5bzGwkcCHQAUjkE912sfdgRWq2/dvD75nkv9+9h+JA/U5Y+3kdf8K++2wUEOHvoq+Ar8xsOnAzBft9aiZBrvgfMysD1AQWkHj9+T2eA6YAb+6nze7+fwI8bmYVCP74+vIw13ZYmdlRBL+TVxC8fm919+HZ2pzF73j+89WINUGn/mVm04DPgepAlXDfQnefGt4uaCM+s4FKFpwwgpmlmFnsqO1l4fZTCD6mWB9BjfHwBvCIu0/Ptr0se05065SnFR0+lxB8jFbb3eu4e01gIcFf/a3Cj5eSCJ7bRJz2kZPcPMe9gZ7AxAQfCfoJOM7MioUfq/4x6oLySEHud06vY9h3n2cBR9meKz1dRgIyswZmVi9mUzPgRwr2+9QXQAkzuwb+dwL50wSfoo0AbrDwJPowVAJsBErnfanxFf7e/YBgKsRu37LnZN0rCd+nwk8z/gs8DwxJ5MEgM6sEvAq86MG1p4cDN+6eM25m9c2sJMHz39nCK8DEPP/7lN9GTa4kmJPWwt13mtki9owEbI9plwkk8lSQvbj7DjO7BOgZ/pIuQvAX5MywyVoz+xYoQ/BxeUJy96UEL8bsngDeNrM7SPC/fmN0BLKf4NAfuBEYF+5rDIwGBuZtaYdPbp5jd59sZhvY/2hJvhW+2W539yXhp2nTgLnAd9FWdngVkn7n9Dq+giCI7NVnd99qZjcBn5nZKoIAkohKAS+E0zl2AfMIpoX0ooC+T7m7m1k74GUze4Bg4HEocC9B5qgPTDMF3flAAAAErUlEQVSzncDrBHOQewHDzGx5gs+zhuCPiFti7t8GvGFm3QmmA10Xs+8/BNP42uRZdfFTPJzqkULws/0O8Ey4rzfBoO0UMzOCfl/k7p9ZcJL9JDPbwZ6fi33KNwvEWHBVkPuAY9z9VjM7neANuG7YZIi7Hx+2vRMo5e4PR1KsSC6ZWRvgTnc/L+pa8gszq0bwkXNDd8+KuJzfzcyaAq+7eyJeCSHXCmu/D8TMSrn7pvCN+SVgrrs/G3Vdh5OZfUXwe21S1LWIRC1fTAXZPfJBcOZpmplNIhi9nhVpYSJyWIUfu04A7kvQUH0DwUlb90ddS14qrP0+SF3DEbGZBFOgXou4HhHJQ/lixFojHyIiIiKS6CIfsdbIh4iIiIgUBPlixFpEREREJNFFPmItIiIiIlIQ5HmwNrOaZjYqXElxppn9NdxewcxGmtnc8Hv5cPsRYftNZvZitmN1DFdonGZmn4WLyoiIiIiI5Lk8nwpiZlWBqu4+xcxKEyz2chHBohFr3L2Hmd0DlHf3u8OLczcnWEr1eHe/JTxOEWAZcJy7rzKzJ4AtugSfiIiIiEQhz0es3X25u08Jb28kWM2pOsGSxm+Hzd4mCNu4+2Z3H0uw3HksC79KhtcLLUMQtEVERERE8lykKy+Gy742J7iObRV3Xw5B+Dazyvt7bLgy443AdGAzwepXNx/WgkVEREREchDZyYtmVopgadjb3X1DLh6fQrA8dHOgGsHSsn+Pa5EiIiIiIgcpkmAdhuL+QD93HxBu/jWcf717HvaKAxymGYC7z/dgovgHwEmHqWQRERERkf2K4qogBvQBfnT3Z2J2fQJcG96+Fhh0gEP9DBxnZpXC+2cSzNcWEREREclzUVwV5BRgDMHc6Kxw870E86w/AGoBi4FL3X1N+JhFBCcnFgXWAW3d/Ydw1ca/AjuBn4BO7r4673ojIiIiIhLQyosiIiIiInGglRdFREREROJAwVpEREREJA4UrEVERERE4kDBWkREREQkDhSsRURERETiQMFaRCRBmFmmmU01s5lm9r2Z3WFm+/09bmZ1zOyKvKpRRKQwU7AWEUkcW929mbs3IlgU6xzgoQM8pg6gYC0ikgd0HWsRkQRhZpvcvVTM/aOAiUBFoDbwDlAy3H2Lu39rZuOBY4GFwNtAT6AH0AYoBrzk7q/lWSdERAowBWsRkQSRPViH29YCDYGNQJa7bzOzekCGu6eZWRvgTnc/L2zfDajs7o+ZWTHgG4KVbhfmaWdERAqgIlEXICIih8TC7ynAi2bWDMgE6ufQvi3QxMwuCe+XBeoRjGiLiMghULAWEUlQ4VSQTGAFwVzrX4GmBOfPbMvpYcCt7j48T4oUESlEdPKiiEgCMrNKwKvAix7M6SsLLHf3LOBqIDlsuhEoHfPQ4cCNZpYSHqe+mZVEREQOmUasRUQSR3Ezm0ow7WMXwcmKz4T7Xgb6m9mlwChgc7h9GrDLzL4H3gKeJ7hSyBQzM2AlcFFedUBEpCDTyYsiIiIiInGgqSAiIiIiInGgYC0iIiIiEgcK1iIiIiIicaBgLSIiIiISBwrWIiIiIiJxoGAtIiIiIhIHCtYiIiIiInHw/1r4/J8hiU9yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Employees'].plot(figsize=(12,5),label='Actual (Test) Data',legend=True)\n",
    "yp.plot(figsize=(12,5),label='Forecasts/Predictions',legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Model didn't see the Test Data (Beyond Jan 2018) at all. But still it was able to make almost accurate forecasts based on what it learnt from the training data. \n",
    "\n",
    "This is purely Data Driven Forecast since everything the model learnt for a successful forecast, was from the Data itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7- Model Evaluation and Error%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mean_squared_error(yp,test['Employees'])**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.813380381169651"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1986.125"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Employees'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "E = 100*rmse/test['Employees'].mean()\n",
    "\n",
    "A = 100 - E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4437475174608673, 99.55625248253914)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E,A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.44 % Error and around 99% Accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "##The End##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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